Reliability and obsolescence of existing structures: evaluation of the seismic vulnerability of Scuola Rossini building

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Corso di Laurea Magistrale in Ingegneria Edile – Architettura

Tesi di Laurea

Candidata: Elena Bertacca

Relatori: Prof. Ing. Pietro Croce

Prof.ssa Ing. Maria Luisa Beconcini Prof. Ing. Dimitris Diamantidis

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This work was developed in collaboration with Hochschule Regensburg University of Applied Sciences, Germany

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Introduction

The continued use of existing structures is of great importance, since the built environment is a huge economic and political asset growing larger every year. The assessment of existing structures is now a major engineering task. The structural engineer is increasingly called upon to devise ways for extending the life of structures whilst observing tight cost constraints, so obsolescence analysis is fundamental in the process of life extending. It is fundamental to establish principles for the assessment of existing structures, since it is a process based on an approach which is obviously different from designing new ones. Moreover, the process of assessment requires knowledge beyond the scope of design codes. Engineers may apply specific methods for assessment in order to save structures and to reduce a client expenditure. The ultimate goal is to limit the interventions to the minimum needed and to consider the various solutions, such as refurbishment, adaptive reuse or demolition; those goals are clearly in agreement with the principles of sustainable development. Moreover, it is worth noticing that demolition is not the only possible solution.

This document is intended as an example of principals and procedures for the obsolescence analysis and the assessment of aging structure. Various concepts are introduced and studied in this research: useful life, physical life and target life, which are compared each other and combined with examples, mainly dealing with the case study.

The aim of this research, that was developed in collaboration with OTH – Ostbayerische Technische

Hochschule of Regensburg and with a meeting at the Klokner Institute - Czech Technical University of Prague, is based on the study and applications of the ISO 13822 (Bases for design of structures), ISO 2394 (General principles on reliability for structures) and on the analysis of Eurocode 8 (Seismic design of

buildings). The basis for reliability assessment is contained in the performance requirements for safety and serviceability of ISO 2394. This Standard enables the possibility to regulate, verify and document the

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adequate safe performance of structures, and also to consider them in a broader sense as part of societal systems; it provides approaches at three levels, namely: risk informed, reliability based and semi-probabilistic approach. The methodical basis for this edition of the ISO 2394 is described in JCSS (2008), JCSS (2001) and EN 1990 (2007).

The building of Scuola Rossini (Firenze) is an adequate case study, since it is an aging and obsolete building; it was subjected to the earthquake of 1919, whose epicentre was in Mugello, and to a flood in 1966. The structure is affected by several decay phenomena that occurred during the years, because of ordinary and extraordinary causes. The goal of the surveys which were conducted on the structure is to understand how and why the deterioration phenomena were originated.

Furthermore, the case study is a significant example since its structure is a mixture between masonry and reinforced concrete frames. Since masonry structures constitute a huge part of the buildings heritage, their assessment is an important engineering task nowadays, and it requires the application of sophisticated analysis methods, based on national building codes and on International Standards. Since the masonry behaves according to a set of specific macro-elements, namely of rigid-bodies, the structural analysis mainly concerns the identification of these macro-elements and the related failure mechanisms activated by specific actions, such as earthquakes. Seismic and kinematic analysis were carried out on the building.

Dealing with seismic and seismic vulnerability too, this research was focused on the seismicity of Firenze: the European requirements are analysed together with the Italian ones; the attenuation laws depending on intensity and acceleration are studied too, and a practical example is provided referring to the earthquake of 1919 in Mugello. The seismic and kinematic analysis bring to a complete picture of the case study, and the seismic risk indexes are provided and discussed too.

The study is organized in accordance with a step-by-step procedure, such as the flow chart of the ISO 13822 suggests: in the first step the preliminary assessment was performed, considering the available documentation and results from inspection and check; the following step concerns a more detailed appraisal, in which the simulated design and the structural analysis are carried out.

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Chapter 1 – Obsolescence

1.1 General and definitions

Buildings are major assets and form a significant part of facility management operations. Although buildings are long lasting they require continual maintenance and restoration. Eventually, they can become inappropriate for their original purpose due to obsolescence, or can become redundant due to change in demand for their service: in such situations, change is likely demolition or refurbishment or reuse (Langston and Lauge-Kristensen, 2002).

For a wide range of reasons buildings can become obsolete long before their physical life has come to an end. Investing in long-lived buildings may be sub-optimal if their useful life falls well short of their physical life. It is wise to design future buildings for change by making them more flexible yet with sufficient structural integrity to support alternative functional use. (Atkinson, 1988) modelled the process of obsolescence and renewal of housing stock, and developed a ‘sinking stack’ theory to explain the phenomenon. Certain layers in the stack represent periods of poor quality construction, and these tend to age more rapidly and absorb greater maintenance resources. Only the top layer grows because it represents the current rate of construction. The net effect is the sinking of the stack, when whether or not maintenance takes place. From an environmental sustainability perspective, it is preferable to minimise new additions to the stack, but at the same time to remove those layers of poorer quality stock that absorb excessive maintenance and operating resources. Increased resources should be allocated to maintenance of those better quality layers of the stack. The philosophy of ‘minimum decay’ (Atkinson, 1988) involves retarding the rate of obsolescence and replacement – slowing down the sinking of the stack by decreasing the consumption of new resources, and assigning increased resources to maintenance and refurbishment.

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As possible solutions, refurbishment often deals with services and technology if outdated. Refurbishment can on itself take many forms, ranging from simple redecoration to major retrofit or reconstruction. On the other hand, adaptive reuse is successful if a particular function is no longer relevant or desired so buildings can be converted to a new purpose altogether. Facility managers are frequently faced with decisions about whether to rent or buy, whether to extend or sell, and whether to refurbish or construct. Usually these are financial decisions, but there are other issues that should bear on the final choice, including environmental and social impacts. (Johnson, 1996) indicates that, as society has advanced, its use of buildings has become more temporal. He states that advances in technology and commerce, including the growth of industrial and office automation, and user demands for more comfortable environments for work and leisure have led to large numbers of buildings becoming obsolete or redundant and these changes have provided an abundance of buildings suitable for rehabilitation and reuse.

Obsolescence is not a necessary condition for demolition, and pretended obsolescence is not necessary always the true reason for pulling down existing building (Thomsen and van der Flier, 2009b). It is not a natural phenomenon but a function of human action. Buildings are complex man-made artefacts and can only survive by means of regular reinvestments during its long service life. As a result the total life cycle costs will generally be a multiple of the initial building costs (Boussabaine and Kirkham, 2004; Woodward, 1997). The related high costs demonstrate the relevance of avoiding and minimizing obsolescence and the need for knowledge how to achieve that.

A building is generally a very durable capital asset. The initial client might only have a limited use for it. Service life planning can facilitate design to enhance the prospects for future sale or re-use by subsequent owners, thereby increasing the residual value of the building.

Matching the component service lives to that of the building reduces the waste at demolition. This is particularly important for temporary buildings. Moreover, the ability to separate the components to leave uncontaminated materials is important for recycling. For many buildings, one external assessment and two internal assessments (for dry and wet areas) can be sufficient. Figure 1.1 simply explains the link between innovation and obsolescence.

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Figure 1.1. Obsolescence under technological innovation (Geng, Dubos, Saleh et al., 2016)

Various definitions of ‘obsolescence’ are given in literature, and many are the concepts related with obsolescence: the service life, the physical life and the target service life.

Firstly, obsolescence is the process of declining performance due to changing of functional, economical, ecological, social, legal, political or physical environment; it reflects the development of the society and environment around the still standing structure (Thomsen and van de Flier, 2011). It is represented by the annual obsolescence rate ω and the physical life time obsolescence factors Oi (Langston, 2011) for

the various types of obsolescence i. (OED, 2010) provides the definition of obsolescence as the process or fact of becoming obsolete or outdated, or of falling into disuse, or more specific the process whereby or state at which machinery, consumer goods, etc., become obsolete as a result of technological advances, changes in demand, etc.

Again, it is obsolete what is no longer used or practised; outmoded, out of date, or worn away, effaced, eroded; worn out, dilapidated, atrophied, no longer current, old-fashioned (M-W, 2010). Housing and property obsolescence is nonetheless a significant design and management issue.

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Obsolescence has been analyzed from a manufacture point of view in relation to the number of units sold over time, that is, in relation to the demand for that specific product, as described by the product lifecycle curve shown in Figure 1.2. Using this model, (Solomon et al., 2000) and (Hatch, 2000) defined obsolescence as a macro-stage that either occurs at the onset of the discontinuance phase, or that overlaps with the last three phases in the traditional life cycle phases of a part, decline, phase-out and discontinuance.

Figure 1.2. Typical component lifecycle and phases (ANSI/EIA, 1997)

According to (Langston, 2011) the annual obsolescence rate is:

ω= ∑ (1.1)

where the sum of all obsolescence factors (O1 physical obsolescence, O2 economic obsolescence, O3

functional obsolescence, O4 technological obsolescence, O5 social obsolescence, O6 legal obsolescence

and O7 political obsolescence) expressed in % as decimal pa is then divided by the physical life TP. Each

obsolescence factor will assume a value belonging to the range 0 – 20%. A normal distribution for life time obsolescence factors Oi was proposed, as shown in Figure 1.3.

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Figure 1.3. Statistical distribution of the obsolescence factors Oi (normal distribution)

Langston’s annual obsolescence rate won’t be normally distributed, since it is the ratio between two normally distributed variables.

According to (ISO 2394, 2002) we can assume the annual obsolescence rate as:

ω≈ (1.2)

(ISO 2394, 2002) defines the annual obsolescence rate as the inverse of the design life. Referring to a design life value of 50 years, ISO provides ω= 2%.

The physical life TP is the theoretical period of time (expected) of a structure fulfilling technical

requirements without considering obsolescence; it depends on environmental context, occupational profile, structural integrity (Langston, 2008). The physical life is a random variable typical ranging from 50 to 200 years; a Weibull distribution for components was proposed (Marteisson, 2003).

On the other hand, analyzing Langston’s database (see Annex A – Figure A.1), a normal distribution for Tp is again an appropriate model.

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The service life or useful life TU is the period of time after installation during which a structure meets or

exceeds the various performance requirements. And more, it is the period of time during which the building and its constituent parts fulfil the requirements for which they were designed, considered as a complex system composed of several sets of interconnected variables and whose links create additional information as a result of interactions (ISO 15686, 2011). Service life is influenced by obsolescence. It may be interpreted as its structural adequacy (Langston, 2008). On the other hand, the definition of the end of service life is a subjective concept that depends on criteria that may change over time. Finally, we can evaluate useful life in the following way, according to (Langston, 2011):

TU=

[ ∑ ( ) ] (1.3)

Finally, the required or target service life TT is the minimum period of time after installation during

which a structure meets or exceeds the performance requirements according to Eurocodes.

Obsolescence may be described as constituting one or more of the following attributes: • physical • economic • functional • technological • social • legal • political • environmental

Every type of obsolescence can be measured by making various examinations. Obviously, obsolescence condition influence the building’s useful life by reducing it. Where high measures are taken against obsolescence, useful life isn’t reduced; in case of normal cures a 10% reduction of useful life can be considered. If insignificant measures are carried out a reduction of 20% of useful life can be provided.

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Firstly, physical obsolescence can be measured by an examination of maintenance policy and performance; if there is no proper maintenance useful life is effectively reduced. Economic obsolescence can be measured by the location of a building to a major city, central business district or other primary market or business hub; useful life is reduced if a building is located in a low density demographic. Functional obsolescence can be measured by determining the extent of flexibility embedded in a building’s design; useful life can be reduced if building layouts are inflexible to change. Technological obsolescence can be measured by the building’s use of operational energy; useful life is reduced if a building is reliant on high levels of energy in order to provide occupant comfort. Social obsolescence can be measured by the relationship between building function and the marketplace; in this case useful life is reduced if building feasibility is based on external income or if the service for which the building is intended is in decline. For example, buildings with an increasing market presence or with fully owned and occupied space receive a 0% reduction; balanced rent and ownership or steady market presence receive a 10% reduction; buildings with fully rented space or with a decreasing market presence receive a 20% reduction.

From a legal point of view, obsolescence can be measured by the quality of the original design; useful life is reduced if buildings are designed and constructed to a low standard. Political aspects of obsolescence are the least publicized concept; they can be measured by the level of public or local community interest surrounding a project. Useful life is reduced if there is a high level of restrictive political interference expected. Indeed, a low level of interest provides a 0% reduction, while a normal public and local community interest leads to a 10% reduction. Finally, in case of high level of interest we have a 20% reduction. A range between -20% and +20% reduction can be considered if a project can receive a significant benefit from political interference rather than a constraint; in this case interference is seen as an advantage, it can extend a building’s useful life and help offset other obsolescence considerations, which are all negative or neutral. Finally, environmental obsolescence is relevant to today’s society and arguably deserving of individual assessment. It can be subsumed within technological obsolescence given the choice of an energy intensity surrogate. As the marketplace continues to become more sustainability-conscious, social, legal and political obsolescence will increasingly reflect the environmental agenda.

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The previous categories of obsolescence described can be seen as important principles for designers (i.e. use of high quality materials, flexibility in spatial layouts, reduced reliance on non-renewable energy). In fact, the ideals of ‘long life, loose fit and low energy’ advocated in architecture schools for centuries is now more important than ever, and coupled with recycling and deconstruction initiatives in buildings that have reached the end of their physical lives, our industry can demonstrate higher sustainability performance. Surrogate estimation techniques were developed to quantify each of the obsolescence categories listed above. Useful life is indeed discounted physical life, and uses the long-established method of discounted cash flow as its basis, where the “discount rate” is taken as the sum of the obsolescence factors per annum. The following assumptions can be found out from the above approach:

1. a maximum scale of 20% is used to judge the impact of each obsolescence category over the building’s physical life;

2. this rate of reduction is uniform each year; 3. each obsolescence category is equally weighted;

4. the rates of obsolescence can be summed across categories, as opposed to selecting the most significant category and ignoring the rest.

1.1.1 Conceptual model

The fact that buildings are composed of a multitude of elements and materials with different life cycle characteristics makes extra confusing complication. For more clarity and better understanding it is necessary to order the causes and effects, the different levels of scale, the building category and type, the kind of tenure and control, the characteristics in general.

Regarding the causes the main differentiation is between physical factors, related to material process, and behavioural factors, related to human actions, and their interactions. Where most of the attention was originally pointed at the physical decay of the buildings and buildings part, the awareness of the behavioural and environmental impact has gradually grown (Nutt et al., 1976). Moreover, often used categorisations of obsolescence distinguish on the one hand internal and external factors, and on the other hand physical and behavioural ones. Internal or endogenous factors are related to the process

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typical for the building itself, while the external or exogenous factors are related to influences for outside. Internal and external factors can match together: Figure 1.4 shows the assembling of those factors in a quadrant mix.

Figure 1.4. Obsolescence, conceptual model (Thomsen, 2011)

The diagonal line from quadrant A to D depicts the increase of complexity regarding scale and participants and the corresponding decrease of control. The physical factors in quadrant A are relatively uncomplicated and can be well controlled and managed by the proprietor. The mainly use related factors in quadrant C are more complex and less easily controlled, while the mainly environmental factors in quadrant B are generally beyond control of the owner, as well as the highly complex factors in quadrant D. From the opposite direction, threats coming from the external behavioural corner can have very serious effects. Where direct control fails, proprietors answers have to be found in timely anticipation and intervention. The interrelation can be demonstrated by looking at the actual environmental challenge of energy efficiency. The energy performance of buildings is on the one hand determined by the energetic quality of the physical design and construction (quadrant A) as measured in

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the EPBD, the Energy Performance of Buildings Directiv (quadrant B), but on the other depending of the users behavior (quadrant C). A low EPBD rating and high energy bill can weaken the market position (quadrant D) and consequently have either a negative impact on the chances for improvements in the direction of C, B and A, resulting in increased obsolescence, or be a stimulant for improvement actions (Thomsen, 2011).

The effects of obsolescence are mostly divided in technical and economic obsolescence (Iselin and Lemer, 1993). Regarding the scale, obsolescence can appear separately or combined on the level of building materials, parts and elements, constructions, separate buildings, blocks, quarters and neighbourhoods.

About building categories, there are many essential differences between residential and non-residential buildings. Housing is a rather stable function with a long life cycle expectancy, while non-residential functions often have a short cycle of usage and adaption, consequently they have different vulnerability for obsolescence (Thomsen, 2011). In relation with building category and tenure, building type has a strong influence on the usage and the appreciation of property. Detached, terraced, multi-storey, high-rise etc. have a significant influence on the property value. The inventory above is not exhaustive; real estate agents will immediately add size, location, situation, architecture, services and facilities (Isaac and Steley, 1999), illustrating the complex influences on property value development as itself a determining variable of obsolescence.

1.2 Obsolescence and the life cycle of buildings

Often obsolescence is regarded as the beginning of the end-of-life phase of buildings. Figure 1.5 provides the relation between obsolescence and service life.

The building and development trade commonly refers to the development cycle, including the design and the construction phases, and the usage phase, which consists of the actual use and the re-use or the end-of-life phase. National building stock statistics often only state withdrawal from the residential stock, in some countries subdivided by withdrawal by demolition and/or disaster merging with other building and loss of function (Dol and Haffner, 2010).

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Figure 1.5. Obsolescence and service life, not in scale (Thomsen, 2011)

Obsolescence can be seen as the extending divergence over time between the declining performance and the steadily rising expectations. Referring to the economic performance of buildings, the growing obsolescence phase predates the final end of life.

Maintenance is required to maintain a building’s initial performance capacity, while improvement and renewal are necessary for rising expectations. For some building categories with short life cycle retail, regular refurbishment and adaption are accepted preconditions to uphold its market position respectively accommodate to changing needs. In other cases, improvement is less obvious due to the absence of urgency. The development of obsolescence is much more complicated and the range of methods and instruments to avoid and cure it likewise broad. Firstly, prevention is the most effective and efficient approach to avoid obsolescence; it consists of systematic periodic analytic anticipation on all influences that are potential threats for the performance of buildings. Four circumstantial factors for decay and obsolescence were founded in the Dutch early post-war housing stock (Lijbers, Thijssen, and Westra, 1984):

1. design – that was by far the main causal factor; 2. construction;

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4. management.

Appropriate functional and circumstantial analyses underlying the functional program, including future developments, and building’s spatial and structural flexibility accommodate future changes are the mainly important aspects.

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Obsolescence diagnosis represents the following step in the systematic periodic analyses of stock performance. Like prevention, diagnosis necessitates attention dealing with early symptoms and trends that may foster negative effects on all quadrants of Figure 1.4, being the base of systematic maintenance and management. This implies the systematic periodical inspection of the building (quadrant A), indications for possible improper use, changing circumstances and conditions (quadrants B, C, D), and fed back as preventive input to be used when programming new developments.

Obsolescence is more and more related to exogenous factors on a larger scale, like unattractiveness of the neighbourhood and/or the availability of more attractive alternative options. An analytical model has been provided by van Kempen, dealing with residential property: the scheme previously provided in Figure 1.6 shows four questions which can have a yes/no answer, and their solutions.

1.2.1 Obsolescence and demolition

The end of life phase is a normal part of the life cycle of buildings; it has already been noticed that buildings necessitate maintenance and refurbishment. Obsolescence is a condition that justifies demolition, but it isn’t a necessary condition. Moreover, demolition is not the only solution: also renovation, adaptive reuse and transformation have to be considered as they extend the service life of buildings. Apart from obsolescence, there can be many reasons to – or not to – demolish. Despite an abundance of case studies and descriptions (Library of Congress, 2010), empirical knowledge about the decision-making in the final phase of the life cycle of buildings and the underlying motives is scarce and fragmented.

Data about demolition of non-residential property are generally not included in the statistics nor available from other resources. As a consequence, quantitative data are mostly available from the residential stock of nineteen out of the twenty-seven EU members that supply any, of which only nine on an a rather complete annual base, while the qualitative knowledge comes almost exclusively from the social rented stock and/or urban renewal areas of these nine countries. Furthermore the definition of what is included in the records varies considerably.

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Looking at the available data previously shown in Figure 1.7, the rate of demolition differs considerably between countries, varying from 0,05% and below in France, the UK and Sweden to over 0,3% in the Netherlands. It is obvious that demolition rate has no constant trend in each country. According to a survey of demolition by housing associations in the Netherlands, over 60% of the demolitions were motivated by functional and structural obsolescence, in the pre-war stock even over 90%. Including economic motives and oversupply, 87% of the demolitions were attributed to a kind of obsolescence and 13% to urban planning (Thomsen and Andeweg-van Battum, 2004). Additional questioning and information showed though that the decision making was also strongly influenced by social problems and more hidden profit driven motives like the land value, urban and asset policy and deals with the municipality and was biased by prejudices about the quality and costs of renewal versus new construction (Thomsen and van der Flier, 2009b).

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So, obsolescence does not necessary lead to demolition, and nor demolition is necessary preceded by obsolescence. Obsolescence is not an inevitable phenomenon but a function of human action, and surely it can be a motive or at least a trigger for the decision between demolition or life cycle extension, depending on the interests, motives and capacities of the proprietor. Physical quality and market demand can be considered as the main decisive variables, with tenure and asset management as main conditional factors. An inverse relation stands between the increase of complexity of types of obsolescence and the decrease of possibilities to manage it. Minimizing obsolescence is important for the preservation of the physical, economical and societal investments involved.

Although the causes for obsolescence in both sectors of the housing market can be the same the decision making in both sectors varies resulting from different objectives and capacities of owner occupiers and professional housing managers. The availability of knowledge and data about management and decision making in both sectors differs too, as relevant knowledge about the owner occupied sector is very scarce compared with the non-profit rented sector.

Different from the non-profit sector the decision making by residents in the owner occupied sector seems to be related with their housing career: decisions are often related to change in household composition or to a move to another dwelling. The decision making in the non-profit sector seems to be mainly related to asset management and policy objectives. Starting from the knowledge about the structure of decision making in this sector it may be fruitful to compare decision making between non-profit housing providers in different housing markets or countries to test the effect of external factors like housing policy, housing culture and housing market.

1.2.2 Statistics of uncertainties in predicting useful life

In collaboration with Prof. Ing. Miroslav Sýkora of the Klokner Intsitute in Prague, statistics of uncertainties in predicting useful life have been provided, referring to Langston’s database (see Annex A – Figure A.1).

A total of 64 projects were identified and compiled into a database for more specific analysis. Many more were found but key information was not readily available. The total number of adaptive reuse

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projects globally was unknown. The projects selected by Langston in 2008 covered a range of building typologies and locations and spanned from an actual useful life between 8 years (built in 2000) and 265 years (built in 1740). The average year of original construction was 1898 and the average year when the project was adaptively reused was 2001, giving a mean difference of 103 years. The projects have been sorted into increasing order based on the percent difference between predicted and actual useful life.

It is worth noticing that for each of the selected projects, predicted useful life was lower than expected physical life: that is due to various matters, such as the one provided by the seven obsolescence factors, and not only to physical aspects. The uncertainty in predicting useful life was calculated as the ratio between actual and predicted useful life:

ϑ= (1.4)

as actual TU is given as the product between uncertainty ϑ and predicted TU.

Langston’s model both underestimated and overestimated useful life, that is we have uncertainty values both higher and lower than one. Log-normal distribution for ϑ is then an appropriate model, since its skewness is about three times the coefficient of variation (JCSS PMC). Among the data an outlier was found; that was confirmed by the following histogram of Figure 1.8.

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Moreover, we clearly noticed that there is no trend in the uncertainty, as we already expected from the analysed data of North America, Finland and Germany, provided in Paragraph 1.2.3. That is again confirmed by the value of the coefficient of proportionality between variability of data and precision of the adopted model R2 which should be about 1, since in our case it is about 0.01 as shown in Figure 1.9.

Figure 1.9. Uncertainty in predicting TU. The linear function found out is y= -0.0008x + 1.1651, R2= 0.0123

The correlation coefficient between predicted TU and the uncertainty ϑ is -0.14. since it has a negative

value, it means an inversely proportionality between predicted TU and ϑ. We are in the low degree of

dependence field, as shown in next Figure 1.10.

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1.2.3 Scuola Rossini analysis

First of all, a simple example is shown in order to evaluate the obsolescence rate. Referring to physical life as 120 years and the sum of the obsolescence factors as 45%, we can calculate the useful life and the obsolescence annual rate values:

• according to Langston (Equation 1.1):

ω= ∑ = ,

! = 0,38% • according to Langston (Equation 1.3):

TU=

[ ∑ ( ) ] = 76,6 years • according to ISO interpration (Equation 1.2):

ω≈ = _"#,#= 1,3%

Concerning the obsolescence annual rates, ISO interpretation and Langston formula lead to different results. ISO values are connected with useful life while Langston results are connected to obsolescence factors and physical life. Moreover, ISO interpretation leads to higher results: that is because Langston’s numerator is lower than ISO one, and Langston denominator is higher than ISO one since physical life TP

is higher than useful life TU. We finally recommend referring to Langston annual obsolescence rate, since

it keeps into consideration every obsolescence factor and physical life.

The same calculations have been applied to Scuola Rossini building (Firenze). The building has been subjected to various works and additions, as shown in Paragraph 3.2.1 – Historical development. The average actual life of the different parts of the building (Part 1 – Part 4) is 68,25 years. It is a masonry building with a reinforced concrete structure in the northern section.

Two tables are shown. Firstly, the obsolescence factors Oi were estimated and a sensitivity analysis

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been done referring pessimistic, typical and optimistic obsolescence factors values, provided in Langston’s database (see Annex A – Figure A.2), as shown in Table 1.2.

Table 1.1. Scuola Rossini evaluation of useful life TU and annual obsolescence factors Oi

Table 1.2. Pessimistic, typical and optimistic values of the annual obsolescence factors Oi proposed by Langston, concerning educational

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Table 1.3. Scuola Rossini evaluation of useful life TU and annual obsolescence factors Oi, referring to Oi values provided in Table 2.2

The useful life results provided in Table 1.2 are lower than the previous ones since the obsolescence factors proposed by Langston are higher than those used in Table 1.1. The most reliable result in Table 1.2 is TU= 76.6 years obtained with the optimistic values of Oi, since each of the repair interventions on

the original building were good for the structure.

It is reasonable assuming 90 years as the useful life of the studied building, which corresponds to the result obtained in Table 1.1 using the recommended value TP= 120 years (values in the green rectangle

in Table 1.1), and which is in accordance with the results shown in next Paragraph 1.2.3 – Analysis of

results about developed and developing countries. The evaluated useful life values are lower than the effective building age, 112 years: the building is living more than what expected. The reasons are the normal maintenance level and the various interventions implemented.

Referring again to Langston’s database results, and to a 12,5% fractile from below and a 87,5% fractile from up, we have:

- estimated TU by Langston for Scuola Rossini (Table 1.1):

TU= 97 years

- estimated actual useful life Tb:

Langston’s estimated TU. Langston’s model uncertainty μϑ= 97 years . 1.03= 100 years

- confidence interval for 75.0%

- confidence interval for uncertainty ϑ (log-normal distribution): lower bound: inverse lognormal cumulative distribution ( $" %

! ; μlnx ; σlnx )= 0.79

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lower bound: estimated actual useful life Tb .

ϑ lower bound= 100 years . 0.79= 79 years upper bound: estimated actual useful life Tb

.

ϑ upper bound= 100 years . 1.28= 128 years That is, a 75% probability of being in the range between 79 and 123 years is obtained referring to Langston’s predicted TU for Scuola Rossini, 97 years.

1.2.4 Analysis of results about developed and developing countries

It is worth analysing the results about observed service life derived from available data, referring to a bachelor thesis under preparation. In total, 225 buildings in North America (2002), 28127 buildings in Finland (2006) and 61835 buildings in Germany (2012 – 2015) have been considered in the statistical analysis. The obtained results are resumed in Table 1.4. The values of percentage referring to buildings older than 50 years are written in grey, since their useful life is more than the target value so they are not kept into consideration in the total results.

The available data about North America were divided into two categories: 122 residential buildings and 103 non-residential buildings. Between them, 53 buildings have been demolished at the age of 38, while 14 were 12 years old when demolished. Consequently 67 buildings younger than 50 years were demolished in 2002 in North America. Finland analysed data are associated to a wider basis: 28127 buildings were classified based on their function and their materials. There were residential, office, public, storage, industrial, transport and ‘other’ buildings, and included timber, concrete, masonry or steel buildings. Among them, 3003 buildings were demolished at the age of 46 years, 2447 at the age of 36 years, 3480 at the age of 26 years, 2027 at the age of 16 years and 954 at the age of 4 years. The remaining ones were older when demolished. The information about German buildings were more recent. They were classified on the basis of their function: residential, institutes, office, agricultural, industrial, commercial and storage, recreational and ‘other’ buildings. Moreover, the results of 2012, 2013, 2014 and 2015 are quite similar. 14784 buildings were demolished in 2012, and 2416 among them were only 32 years old. In 2013, 2689 of the 15859 buildings demolished were 33 years old. 14784 buildings were demolished in 2014, and 2398 of them were 34 years old. Finally, 16408 buildings were demolished in 2015 and 2712 of them were 35 years old.

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The percentage of TU ≤ TT was then calculated: that is the percentage that useful lives are less than the

target life provided by Eurocodes, TT= 50 years. As a first evaluation, they were simply obtained by

dividing the number of buildings demolished before 50 years of life by the total number of available buildings. The obtained values are resumed in Table 1.5. Moreover, Germany results were almost homogeneous (P[TU ≤ TT] were 16,34%, 16,96%, 16,22% and 16,53% respectively for 2012, 2013,

2014and 2015 data). So 2012 – 2015 Germany percentages are provided together.

Germany trend is more or less constant in the different years, where the standard deviation in the years 2012-2015 is only 32,2%. In Finland the percentage is considerably higher than in Germany. North America result stands between the finnish and german ones. It is worth noticing that only 225 american buildings data were available while 28127 Finnish buildings and 61835 German buildings were studied. Finally, among the studied buildings only 0,25% were American, 31,19% were Finnish and 68,56% were German ones. The key factors influencing the results are maintenance level, environment and construction materials.

North America 2002 P[Tu ≤ Tt]= 29,78%

Finland 2006 P[Tu ≤ Tt]= 42,35%

Germany 2012-2015 P[Tu ≤ Tt]= 16,52%

Table 1.5. Sums of P[Tu ≤ Tt]

Figure 1.11 resumes the previous results, in which the histogram shows the percentages of buildings demolished before 50 years from their construction.

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Figure 1.11. Histogram of percentage of useful life being lower than target life, P[TU ≤ TT] in the analysed countries

Figure 1.12 shows the cumulative distributions. Those graphics have been obtained by summing the percentage of demolished buildings for a certain age value to the previous values available. That is, for example regarding North America: 6,22% of the buildings have been demolished at the age of 12 years and 23,56% at the age of 38 years, so 38 years value corresponds to 6,22% + 23,56%, and so on. Germany results from 2012 to 2015 have been combined. Obviously, the latest age values correspond to 100% probability.

The percentages corresponding to 50 years of age value have been acquired: in North America it is 41,2%,in Finland 50,0% and in Germany 24,4%.

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Figure 1.12. Cumulative distributions graphics of the observed service life

Available data don’t provide the number of demolished buildings each year, but only the number of demolished structures in certain years. That is, we have ‘age ranges’ and the number of demolished buildings for each year since 2002, 2006 or 2012-2015 were not at disposal.

Through a linear interpolation we can obtain the number of demolished buildings aging 50 years. In North America 48 over 225 buildings could be demolished when they were 50 years old, that is the 21,3% of the total available buildings; in Finland 3698 over the 28127 available buildings could have been demolished while aging 50 years old, that is the 13,2% of the total number. Finally, in Germany in 2012 they would have been 6048 over 14784 that is the 40,9%; in 2013, 6429 buildings over 15859 that is the 40,5%, in 2014 5848 buildings over 14784 that is the 39,6%; in 2015, 6482 buildings over 16408 that is the 39,5%, so Germany 2012-2015 mean value is 40,1%.

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Concluding, Germany and Finland data are almost wide. On the contrary, North America data are insufficient to consider them as American tendency. German data from 2012 until 2015 are quite similar, so we can consider German trend as constant.

Moreover, each analysed country subdivided the buildings in ‘residential’ and ‘non-residential’ classes. Specifically, Finland and German distinguished the various functions among the ‘non-residential’ buildings: office, industrial, public, storage and transport buildings among Finnish data, and office, industrial, institutes, cultural, agricultural, recreational buildings among German data.

Regarding materials, only Finland recorded such categories: timber, concrete, brick and steel buildings. Mean age values were provided basing on materials: 65 years for timber, 50 years for concrete, 51 years for brick and 35 years for steel buildings. So, we can conclude that Finland’s demolition activities are much higher than the German and American ones. That is probably due to the high presence of timber buildings in Finland. Finland probability of useful life being as the target value provided by codes is the highest one (50,0%): North America result is lower, while German probability is the lowest one. That confirms the probabilities sum showed in Table 2.4, where German probabilities have the lowest values and Finnish ones are the highest. Founding out the values corresponding to 50 years age was possible through the cumulative distributions graphics: in fact, the available data didn’t provide this result but the number of demolished buildings belonging to a range of years. The ages we found from available data are the ones shown in the ‘Age’ columns of Table 2.4 and on the horizontal axis of cumulative distributions graphics.

North America’s and Germany’s mean useful life values are quite similar, while Finland’s mean value is lower: this can confirm the high demolition activity in Finland. Obsolescence annual rate values according to ISO (Equation 1.2) and coefficients of variation coefficients of variation are low.

It would be interesting comparing the above discussed results with data about developing countries. However, such data are not wide and not easy to find out. Researches have provided few data about India, Thailand, Tanzania, Tunisia, Israel, and Kenya: they deal with numbers of demolished buildings, but neither buildings’ age nor them characteristics, materials and functions.

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Indian 2000 data (Sandeep Shrivastava and Abdol Chini M.E. Rinker Sr., School of Building Construction University of Florida, USA) provide quantities of various materials generated per year from demolition. Moreover, a comparison between Indian and American results from demolition waste data is provided. No more information about demolished buildings have been recorded.

Thailand data (Workshop documentation by Dr. Maike Hora, 2000) provide the average generation rates of demolition waste, from residential and non-residential demolition. Tanzanian data provide the number of demolitions, which were probably needed in order to acquire materials for new constructions (Mwita Sabai, Prof. Jos Lichtenberg, Dr. Emilia Egmond, Dr. Rubhera Mato, and. Dr Joseph Ngowi. Technische Universiteit - Eindhoven, Ardhi University - Dar es Salaam, 2010). Also data about Israel only show the number of demolitions (The guardian, 2015). Tunisian data are recorded by SNIT (Société Nationale Immobilière de Tunisie). They concern demolition of oukalas (old trading khans) and of rudimentary housing in order to relocate inhabitants in new units (WHO World Health Organization, 2011). No numbers are provided.

Found data only provided weights and volumes of demolished materials, or generic number of demolitions. No conclusions could be obtained about useful life TU and obsolescence factors Oi: TU

values are expected to be lower and Oi higher than developed countries’ results, because of poor design

and construction and economic, political and social reasons. Probably, many of the happened demolitions in developing countries have not been authorized: that could be a reason why they do not record and collect data as developed countries do.

Finally, referring to the results obtained, the percentage of useful life being lower than the target value, referring to normally distributed variables, was calculated. The limit state function g is defined as:

g= TU – TT= TU – 50 years

μg= μTU – μTT= 98,1 years – 50 years= 48,1 years

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31 z= $ /0 ₁₀ = -1,34

P [TU≤ 50 years]= P [g≤0]= Φ (-1,34)= 9,01%

where TU values are obtained from Langston’s database and TT is 50 years.

1.3 Obsolescence limit states

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The limit states of obsolescence are quite different from the others because they often can not be described in quantitative ways. All classes of limit state are provided in Table 1.6 above, while Table 1.7 provides the analogies between the mechanical, durability and obsolescence performance modelling and design.

Obsolescence limit state is aiming to guarantee the ability of the buildings and civil infrastructures to have an ability to meet all current and changing requirements with minor changes of the facilities, thus avoiding the need of early renewal or demolition. The signs about obsolescence are normally found outside of the facility. The decision when those obsolescence indicators have increased excessively, meaning that the limit states have been reached, is in most cases organisation-specific.

However, some qualitative limit states of obsolescence can be defined on generic level. These are presented in Table 1.8.

Table 1.7. Comparison of static and dynamic (mechanical) limit state method, the degradation limit state method and obsolescence limit state (Sarja, 2015)

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Table 1.8. Functional level usability limit states of obsolescence of structures (Sarja, 2015)

For each proposed alternative of design or Maintenance, Repair and Rehabilitation (MR&R) solution, the following obsolescence procedure are usually made: firstly, the relevant obsolescence factors is identified and the relevant obsolescence limit states is analysed. Evaluation methods will be selected for the relevant potential obsolescence cases. Then two evaluations are made: one for the characteristic service life against the actual modes of the obsolescence, and another one for the required lifetime safety factors for each mode of obsolescence. The modes of the obsolescence and the corresponding values of the design service life are then listed, and the results are moved into the general design or MR&R planning procedure.

Several of the general methods of lifetime design and MR&R planning, such as the Quality Function Deployment method (QFD), the Life Cycle Costing method (LCC), the Multi Attribute Decision Aid (MADA) and the Fault Tree Analysis (FTA), can be applied for obsolescence design.

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The integrated lifetime reliability and the limit state approach are building an essential core of the integrated life cycle design and lifetime management, MR&R planning. Performance based on modelling includes the following three classes:

1. static and dynamic (mechanical) modelling and design;

2. degradation based durability and service life modelling and design; 3. obsolescence based performance and service life modelling and design.

Optimising and controlling lifetime quality of buildings or civil infrastructures are the objective of the integrated life cycle design, in relation to the generic requirements listed in Table 1.9.

Table 1.9. Generic classified requirements of buildings and civil infrastructures (Sarja, 2015)

The lifetime quality means the capability of the structures to fulfil the multiple requirements of the users, owners and society in an optimised way during the entire design or planning period, usually from 50 up to 100 years. In the next two Figures 1.13 and 1.14, degradation and obsolescence related performance modelling of structures are shown.

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Figure 1.14. Obsolescence related performance modelling of structures (Sarja, 2015)

The QFD method means building of a matrix between requirements and performance properties or technical specifications. In the obsolescence issues QFD can be used for optimising the technical specifications and/or performance properties in comparison to changing requirements and their changing ranking and weights. The obsolescence analysis and decision making procedure includes following steps:

1. define the individual requirements corresponding to alternative obsolescence assumptions; 2. aggregate the individual requirements into primary requirements;

3. define the priorities of primary requirements of the object for alternative obsolescence assumptions;

4. define the ranking of alternative solutions for avoiding the obsolescence; one of these solutions is the demolition;

5. select between these alternatives using the priorities from step 2;

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Life Cycle Costing can be effectively used in obsolescence analysis and decision making between alternative obsolescence avoidance strategies and actions. It can be used either alone, focusing on economic obsolescence options, or as one part of the multi-criteria analysis and decision making, connected to the other methods (QFD, MADA or FTA). In obsolescence issues the alternatives are different obsolescence options, and alternative strategies and actions for avoiding the economic obsolescence.

In order to measure the influence of obsolescence factors and options into the ranking and choice between alternative strategies and actions for avoiding obsolescence, the method of sensitivity analysis of MADA can be applied. Sensitivity analysis with Monte-Carlo simulation consists then in four steps, as shown in Figure 1.15:

1. random assessment of weights or alternatives assessments simulating small variations; 2. application of the MADA methodology;

3. ranking of alternatives;

4. statistical analysis of the various rankings.

Figure 1.15. Monte-Carlo simulation in sensitivity analysis of MADA (Lifecon Deliverable D2.3)

Like any other risk analysis method, FTA starts with the description of the system, or project, process, etc., where the fault tree is going to be applied. The bounds of the system and the level of complexity must be clearly defined. In short, the risk assessment and control procedure can be described with the following four steps:

1. identification of adverse incidents;

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- deductively (downwards), in order to find causes, - inductively (upwards), in order to find consequences; 3. quantitative risk analysis;

4. risk-based decision making, and continuous updating of risk database.

In obsolescence analysis the adverse incidents mean unwanted obsolescence indicators (i.e. service capability of a bridge is too low for increased traffic). The steps 1, 2 and 4 are always performed if risk analysis is used, forming qualitative risk analysis. The step 3 is only performed if qualitative risk analysis is not enough for decision making and if quantification is possible. The upward analysis – to find consequences for the identified adverse incidents – is made using event tree analysis (ETA).

1.4 Adaptive reuse

Building adaptive reuse is an important global topic. It is increasingly being applied as a solution to urban renewal where existing facilities have become obsolete but where heritage values deserve to be protected and where significant physical life remains embedded in their structure and materials. Revitalisation of buildings in this context is a valid response to climate change and sustainability agenda, as it has the potential to reuse a large proportion of resources in place without destruction or substantial replacement. Adaptive reuse needs to be planned at the outset, and if this is done wisely and routinely, it will provide a means of realizing sustainability objectives without reducing investment levels or economic viability for the industry. In fact, adaptive reuse is the future of the construction industry. It is an intervention strategy to ensure that collective social value is optimised and future redundancy is planned, and it deals with generic application to all countries and all buildings.

Adaptive reuse requires an estimate of the expected physical life of the building and its current age, and also an assessment of physical, economic, functional, technological, social, legal and political obsolescence which is undertaken using surrogate estimation techniques as no direct market evidence exists. Existing buildings that are obsolete or rapidly approaching disuse and potential demolition are a ‘mine’ of raw materials for new projects: a concept described by Chusid (1993) as ‘urban ore’. The attitude is the so-called ‘adaptive-reuse’: raw materials obtained during demolition aren’t assigned to new applications, but the feeling is to leave the basic structure and fabric of the building intact, and

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change its use. Adaptive reuse has great application to international efforts to conserve resources through more sustainable practice.

Archetypes are constructed for ten generic facility classifications as a means of understanding the potential value and risk of adaptive reuse intervention. The outcomes indicate that each facility type has a distinctive pattern that can be used to inform strategic decision-making at an early stage. Facility classification is shown to be a key factor in adaptive reuse success. The potential is that there’s a propensity for projects to realise economic – social – environmental benefits when adaptive reuse is implemented. Retail, healthcare and landmark are more attractive as potential adaptive reuse projects than residential, religious, industrial facilities, they have lower uncertainty than the reminder, so a higher level of confidence in the prediction, and they have a low skew value, which makes them attractive since earlier relative intervention is likely. Finally, changing the class of a building, the so-called functional classification, is another solution. That way may also be attractive for clear economic, environmental and social benefits.

Adaptive reuse potential (ARP) model is applied to construct archetypes or patterns for various facility categories to provide insight into project feasibility decisions. ARP model can be used to rank and prioritise projects for renewal. Program Evaluation and Review Technique (PERT) analysis for a range of obsolescence rates tests the probability of success.

ARP model has generic application to all countries and all building typologies. It requires an estimate of the expected physical life and the current age of the building both reported in years and an assessment of physical, economic, functional, technological, social, legal and political obsolescence. Obsolescence is advanced as a suitable method to reduce expected physical life in order to calculate objectively the useful life of a building. An algorithm is developed that takes this information and produces an index of reuse potential expressed as a percentage. Existing buildings in an organization’s portfolio, or existing buildings across a city or territory, can therefore be ranked according to the potential they offer for adaptive reuse. Where the current building age is close to and less than the useful life, the model identifies that planning should commence. Its application was first demonstrated for a real case study in Hong Kong (Langston and Shen, 2007).

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To assist in the forecast of physical life, a calculation template has been developed, as shown in Figure 1.16. A series of questions gives insight into the longevity of a building according to three primary criteria: namely environmental context, occupational profile and structural integrity. Each category is equally weighted, and comprises ten questions requiring simple yes/no answers. Where information is unknown, a blank answer (no response) is then ignored in the calculation. Three questions under each primary criterion are double weighted due to their relative importance. Some conservatism is applied to the estimate and the forecast is rounded down to one of the following outcomes: 25, 50, 75, 100, 150, 200, 250 or 300 years. The template is unsuitable for temporary structures or for iconic monuments that both require specialist judgment.

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Figure 1.16. Physical life worksheet. Project name: Melbourne GPO comprising concrete structures and massive stone-faced masonry walls, steel roof framing with glass vaulted ceiling, large open plan atrium and perimeter offices. Questions indicated (#) are double weighted

(Langston, 2008)

No restrictions were introduced other than temporary structures and ancient monuments were avoided, as the physical life calculator is not applicable for these projects. Hence Scuola Rossini building is suitable for Langston’s calculator application as shown in Figure 1.17. Anyway we refer to the recommended value TP= 120 years.

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Figure 1.17. Physical life worksheet applied to Scuola Rossini

The annual obsolescence rate across all criteria is the ‘discount rate’ that performs this transformation. The decay curve, as shown in Figure 1.18, can be reset by strategic capital investment during a renewal process by the current owner, or a future developer, at key intervals during a building life cycle.

An algorithm based on a standard decay (negative exponential) curve produces an index of reuse potential, the ARP score, and is expressed as a percentage:

ARP (increasing)= $

2 34 55

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43 ARP (decreasing)= $

2 34 55

$6 3 (100 – ETb) (1.6)

where ETU, ETb and ETP are respectively the effective useful life, the effective building age and the

effective physical life, that can be obtained as successively provided in Equations 1.7, 1.8 and 1.9.

Figure 1.18. Adaptive reuse potential model (Langston, 2008)

The following ARP scores can be considered as:

- > 50%: high adaptive reuse potential; - 20% - 50%: moderate potential;

- < 20%: low value; it represents about one-third of the area under the decay curve in each case.

The estimation of expected physical life is the starting point for the calculation of useful life. Useful life is then determined through application of Equation 1.3, which confirms the notion that useful life is

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indeed discounted physical, and uses the long-established method of discounted cash flow as its basis, where the ‘discount rate’ is taken as the sum of the obsolescence factor per annum.

Moreover, we can obtain values for the effective useful life ETU, for the effective building age ETb and for

the effective physical life ETP in the following ways:

ETU = (1.7)

ETb = 7 (1.8)

ETP = (1.9)

The ARP function profile is a function of obsolescence factors that are deemed to apply. They are understood as ranges within which reasonable estimates occur. High rates of obsolescence mean lower useful lives and ARP profiles skewed towards the short term, while low rates of obsolescence mean higher useful lives and ARP profile skewed towards the long term. In the latter case, ARP scores are lower as the point of optimal intervention is delayed and leaves relatively little time to enjoy the benefits of the new purpose before the end of the facility’s life cycle.

ARP profiles can be readily created for a range of obsolescence values for any facility. An Australian Research Council (2008-2010) has shown that high ARP scores lead to superior economic, social and environmental benefits in practice.

The PERT analysis is applied to assess the range of obsolescence values that could be reasonably be expected for each facility classification. In order to apply PERT analysis in a unique manner to assess the range of obsolescence values that could reasonably be expected for each facility classification. This involves estimating the typical value for each obsolescence category, as well as the pessimistic (worst case) and optimistic (best case) values. They are combined using a simplified beta distribution to calculate the most likely value te:

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te= (89: 9;<=90< 8>?)_# (1.10)

The variance s is provided by the following equation:

s= [ (@ A $@ B) ]_# 4 (1.11)

Finally, the probability z is used to indicate the extent of skew of the most likely value to wither the pessimistic or the optimistic values. It is based on normal distribution and it is obtained as:

z= (∑ C D $ ∑ E)

√G (1.12)

In Equations 1.10, 1.11 and 1.12, max and min are respectively the maximum and minimum values,

and average is the typical value. Reasonable assumptions are made regarding the description of generic facility classifications. The obsolescence factors are described using either 0, 5, 10, 15 or 20% for each of physical, economic, functional, technological, social and legal obsolescence categories, and -20, -15, -10, -5, 0, 5, 10, 15 or 20% for the political obsolescence category as per the ARP model definition. High values indicate that more premature obsolescence is expected (Langston, 2011). The facility classifications included in Langston paper comprise ten of the most common building typologies:

• commercial (based on an office tower); • residential (based on a detached house); • retail (based on a shopping centre); • industrial (based on a warehouse); • landmark (based on a museum); • civic (based on a community centre); • recreational (based on a hotel); • healthcare (based on a hospital); • educational (based on a school); • religious (based on a church).

Reliability and obsolescence of existing structures: evaluation of the seismic vulnerability of Scuola Rossini building

Contents

Introduction

Chapter 1 – Obsolescence

1.1 General and definitions

1.2

Obsolescence and the life cycle of buildings

1.3

Obsolescence limit states

1.4

Adaptive reuse